Face detection: How computer vision enables facial recognition
Computer vision technology is developing at a rapid rate. As a consequence, the use of CV spread to many areas. One of them is facial recognition. As research shows, the facial recognition market was valued at $ 3.83 billion in 2020. Moreover, according to forecasts, it could reach $ 16.74 billion by 2030.[1] Law enforcement, governments, the defense sector, and large companies want to increase security and defense. Thus, they are investing more and more resources in implementing facial recognition technology.
In the article below, we focus on how a CV enables facial recognition.
What is computer vision?
Let’s start first with a brief definition of what computer vision is. Overall, it is the field of AI that helps computers extract information from images, videos, and other visual inputs. Thus, it helps computers interpret and understand the visual world. It is worth mentioning that a CV can identify and process images in a way similar to human eyesight.
In a word, AI enables computers to think. At the same time, computer vision enables observation and understanding of the surrounding world. Find out more about computer vision solutions.
The application of computer vision methods covers many areas. One of them is object detection. The computer extracts pixels and runs them through machine learning or deep learning techniques. Face recognition is s common example of object detection.
Facial recognition – what is it, and how does it work?
FACE RECOGNITION
Face recognition is a way to verify a person’s identity from a digital image of their face. The use of facial recognition systems enables the identification of individuals in photos, videos, or in real-time as well.
3 STAGES OF FACIAL RECOGNITION
- Face Detection
Face detection is the process of locating a face in an image. Apart from people, paintings often have other objects, landscapes, buildings, and more. Computer vision can distinguish a face from other objects. What’s more, it enables the detection of face data not only from the front but also in the profile. Machines that use computer vision can identify objects with an accuracy comparable to humans.
- Face Analysis
After detection, the analysis process follows. This is the stage at which the facial recognition system analyzes the image of the face. Algorithms start by searching for human eyes, then may try to detect eyebrows, mouth, nose, and iris. These are the key elements that distinguish a face from other objects. Face recognition technology determines the distance between the eyes, from the forehead to the chin, and between the nose and mouth. Then it assesses the eye sockets’ depth and the cheekbones’ shape. The last phase is to determine the shape of the mouth, ears, and chin.
Each person has a unique fingerprint. The same thing happens with our faces. From the collected data, the system transforms them into a set of digital information known as a face print.
- Face Recognition
The last step is recognizing or finding a match. The algorithm compares the face print with the database of other faces. For example, when sharing a photo on Facebook, you should consider that it becomes part of the base of this page and can be used to recognize faces. Recognition is when a face print matches an image in the database.
How to implement – facial recognition Python
One of the most trusted programming languages is Python. It is the main language used to build computer vision solutions. Many large companies, such as NASA, Google, etc., are using this program. As complicated as it may seem, implementing facial recognition isn’t that difficult. This can be easily done using Python.
Python owes its popularity to the following elements:
- A huge number of libraries
- Dynamically growing community
- The relatively flat learning curve
Facial emotion recognition
When talking about face recognition, it is also impossible to mention emotion recognition. Can computers read emotions from people’s faces?
Facial emotion recognition (FER) is a technology that enables the analysis of facial expressions from images and videos to reveal human emotions. As research shows, recognizing emotions is becoming more and more popular. By 2026, the global emotion detection market will grow to $ 37.1 billion.[2]
However, unless AI is trained, it will not be able to detect emotions. So you need a huge amount of data to train the algorithm well so that it can recognize human emotions. Using AI-powered face recognition technology, the system can identify the following six categories:
- Anger
- Deceit
- Disgust
- Fear
- Happiness
- Sadness
- Surprise
To detect people’s emotions, we – humans pay attention to the nose, eyebrows, lips, eyes, etc. So, if we see a smile, we consider the person happy. Computer vision algorithms are doing the same.
Conclusion
In conclusion, computer vision is a breakthrough and convenient technology. It has many exciting application areas, e.g., face recognition. This technology maps facial features and helps to identify a person from the stored facial print data.
[1]https://www.alliedmarketresearch.com/facial-recognition-market#:~:text=Facial%20recognition%20market%20is%20expected,with%20the%20stored%20facial%20database
[2] https://www.prnewswire.com/news-releases/emotion-detection-and-recognition-market-worth-37-1-billion-by-2026–exclusive-report-by-marketsandmarkets-301256671.html